Summary
Schizophrenia (SCZ), bipolar disorder (BP), and Major Depressive Disorder (MD) are three major psychiatric disorders that affect mood, thinking, and behavior. These disorders are strongly genetically intercorrelated, and exhibit pronounced clinical overlap, suggesting that they are different manifestations of a shared underlying neurobiology, along a spectrum. However, the neurobiological mechanisms and pathophysiology of these disorders are still poorly understood, limiting effective drug discovery. There is an urgent need for new models for drug testing, as animal models have major limitations, and current psychiatric organoids systems are dependent on patient derived stem cells, in which technical, genetic and biological diversity confound the interpretation, and obscure the underlying neuropathology. The major challenge of this ERC proposal is to develop organoid models for psychiatric disorders by targeting upstream transcription factors. Transcription factors are key molecules that drive cell type differentiation including neuronal network formation. Hence, the central hypothesis of this ERC proposal is that neuronal subtype associated transcription factors can function as targets to model these disorders using brain organoids. However, the neuronal transcriptional regulators that mediate these disorders are currently unknown, and difficult to identify. Novel genomics technologies allow for an unbiased characterization of the brain in order to detect cell types and genes that are dysregulated in disease, and can be used to identify putative upstream transcription factors. Here, I propose a selection of multi-omics profiling - strategies to a unique collection of high quality psychiatric human brain tissue aimed at reliably identifying key upstream transcription factors. We will subsequently target those transcription factors in brain organoid systems to establish neurobiological models of these disorders.
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| Web resources: | https://cordis.europa.eu/project/id/101078437 |
| Start date: | 01-08-2023 |
| End date: | 31-07-2028 |
| Total budget - Public funding: | 1 499 999,00 Euro - 1 499 999,00 Euro |
Cordis data
Original description
Schizophrenia (SCZ), bipolar disorder (BP), and Major Depressive Disorder (MD) are three major psychiatric disorders that affect mood, thinking, and behavior. These disorders are strongly genetically intercorrelated, and exhibit pronounced clinical overlap, suggesting that they are different manifestations of a shared underlying neurobiology, along a spectrum. However, the neurobiological mechanisms and pathophysiology of these disorders are still poorly understood, limiting effective drug discovery. There is an urgent need for new models for drug testing, as animal models have major limitations, and current psychiatric organoids systems are dependent on patient derived stem cells, in which technical, genetic and biological diversity confound the interpretation, and obscure the underlying neuropathology. The major challenge of this ERC proposal is to develop organoid models for psychiatric disorders by targeting upstream transcription factors. Transcription factors are key molecules that drive cell type differentiation including neuronal network formation. Hence, the central hypothesis of this ERC proposal is that neuronal subtype associated transcription factors can function as targets to model these disorders using brain organoids. However, the neuronal transcriptional regulators that mediate these disorders are currently unknown, and difficult to identify. Novel genomics technologies allow for an unbiased characterization of the brain in order to detect cell types and genes that are dysregulated in disease, and can be used to identify putative upstream transcription factors. Here, I propose a selection of multi-omics profiling - strategies to a unique collection of high quality psychiatric human brain tissue aimed at reliably identifying key upstream transcription factors. We will subsequently target those transcription factors in brain organoid systems to establish neurobiological models of these disorders.Status
SIGNEDCall topic
ERC-2022-STGUpdate Date
31-07-2023
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